Structify

Structify is a data and AI company that positions itself as the foundation layer for AI adoption at established, non-digital-native businesses. Its stated engagement model is a fixed 30-day, flat-cost project: days 0-14 are spent connecting a customer's data sources and building an understanding of business context; days 14-30 produce a "blueprint" identifying where AI can drive measurable return on investment. An optional third phase (days 30-90) builds AI solutions, or Structify hands the blueprint to the customer's team or a partner, followed by ongoing monitoring and maintenance.

The underlying product is described as a suite of AI data agents and tools spanning a full "AI data stack." At the base is a context layer: connectors to systems (the platform page cites 3,000+ connectable systems, with examples including Snowflake, Postgres, MSSQL, HubSpot and Stripe), automatically maintained "data maps" functioning as an org-wide data dictionary, and a "context handbook" of shared metric definitions. On top sit natural-language question answering (including via Slack and Teams), visualizations, and data cleaning, enrichment and deduplication. The top layer covers scheduled reports and automations that push results back into CRMs, warehouses and internal tools. Documentation additionally describes AI-powered web scraping and PDF extraction, flexible prompt-defined schemas, and code-generated data pipelines whose generated code users can inspect.

Earlier public coverage frames the company more narrowly as a tool that turns arbitrary web, document and internal sources into customized structured datasets, with users specifying a schema, choosing sources and deploying agents to extract the data. The founder describes the core innovation as code generation that automates extract-transform-load work, with the aim of giving non-technical users the capabilities of a data engineer.

Founding story

Founder Alex Reichenbach's background is in machine learning research, with a first patent awarded in 2017, academic work in the Krishnaswamy labs applying ML and AI to single-cell RNA drug discovery, and a stint in a computer vision lab at the robotics company Matician. He subsequently joined a small investment bank, where he observed the data team struggling to obtain needed information and concluded that much of the data engineering work could be automated with AI and ML — the insight that led to Structify. The company's own account of its origin says its team repeatedly saw organizations that had data but no shared understanding of how it fit together, prompting them to build tooling that maps how data connects and defines what it means.

Business model

Structify sells a defined-outcome engagement rather than hourly consulting: a flat-cost, 30-day project that produces a technical data foundation and an AI blueprint, with optional follow-on build work over the subsequent 60 days or ongoing support and maintenance. It also offers a self-serve software platform with a free trial and account sign-up, and markets a "platform for partners."

Sources indicate a flat fee for the defined 30-day engagement (explicitly contrasted with consultants billed by the hour), plus platform access with a "Try for free" entry point and paid accounts; specific pricing is not disclosed in the sources.

Traction

Published customer references include LP Aero (cited 80% reduction in data entry time), Airgoods (50% reduction in manual data work), SumUp (50% higher enrichment quality), Harney & Sons Fine Teas, CandidPro, WithMe and EthoSystems. FinSMEs reported adoption across finance, construction and enterprise tech at the time of the seed round, and the founder cited customers ranging from construction to finance to deep-sea mining. The company lists SOC 2 Type II certification and HIPAA, CMMC and GDPR compliance. Prior to significant venture funding, AWS and Google together provided $700,000 in compute credits.

Latest developments

The most recent dated source is a December 5, 2025 Engine #StartupsEverywhere profile of co-founder Alex Reichenbach, in which he describes automating the data engineer role via code generation, the use of Claude plus in-house fine-tuned scraping models, reliance on cloud compute credits, and advocacy for STEM talent funding and New York's state matching program. Current site material describes the 30-day blueprint offering, a partner platform, 3,000+ connectors, and a set of security certifications.